JOURNAL ARTICLE
Thermoeconomic Analysis and Optimization of a Combined Supercritical CO2 Recompression Brayton/Kalina Cycle Driven by Boil‐Off Gas Combustion and Liquefied Natural Gas Cold Energy.
Published In: Energy Technology, 2023, v. 11, n. 9. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Pan, Jie; Zhu, Min; Li, Mofan; Li, Ran; Tang, Linghong; Bai, Junhua 3 of 3
Abstract
The energy and costs required to reliquefy the boil‐off gas (BOG) produced in liquefied natural gas (LNG) tanks are enormous. At the same time, LNG releases large amounts of cold energy during the regasification process. To recover both the BOG and the LNG cold energy for power generation, herein, a novel system consisting of a supercritical carbon dioxide recompression Brayton cycle, an ammonia–water mixture Kalina cycle, a BOG combustion turbine cycle, and a natural gas turbine cycle is presented. The genetic algorithm with multiple elite saving is used to optimize the system thermoeconomic performance, and the maximum energy efficiency, exergy efficiency, and net present value of 65.49%, 33.88%, and 7.374 × 107 $ are obtained respectively. The nondominated sorting genetic algorithm II is implemented to achieve the multiobjective optimization, and the comprehensive performance analysis is carried out on this basis. The results show that heat exchangers and turbines account for the highest exergy destruction and total cost proportion with the value of 76.5% and 81.1% respectively. The heat transfer curves between LNG and working fluids are well matched, and the cooling capacity for ammonia liquefaction is the largest, accounting for 66.66%, which also generates 581.2 kW of power generation. The results also indicate that the LNG cold energy cascade utilization system combined with BOG combustion has excellent thermodynamic performance and can obtain ideal economic benefits. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Energy Technology. 2023/09, Vol. 11, Issue 9, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2023
- ISSN:21944288
- DOI:10.1002/ente.202300480
- Accession Number:171810847
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